{% set version = '2.0-18' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-glmnet
  version: {{ version|replace("-", "_") }}

source:
  url:
    - {{ cran_mirror }}/src/contrib/glmnet_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/glmnet/glmnet_{{ version }}.tar.gz
  sha256: e8dce9d7b8105f9cc18ba981d420de64a53b09abee219660d3612915d554256b

build:
  merge_build_host: True  # [win]
  number: 2
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}              # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('fortran') }}        # [not win]
    - {{ compiler('m2w64_fortran') }}  # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}make
    - {{posix}}sed               # [win]
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]
  host:
    - r-base
    - r-matrix >=1.0_6
    - r-foreach
  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-matrix >=1.0_6
    - r-foreach

test:
  commands:
    - $R -e "library('glmnet')"           # [not win]
    - "\"%R%\" -e \"library('glmnet')\""  # [win]

about:
  home: http://www.jstatsoft.org/v33/i01/.
  license: GPL-2
  summary: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization
    path for linear regression, logistic and multinomial regression models, Poisson
    regression and the Cox model. Two recent additions are the multiple-response Gaussian,
    and the grouped multinomial regression. The algorithm uses cyclical coordinate descent
    in a path-wise fashion, as described in the paper linked to via the URL below.
  license_family: GPL2
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

extra:
  recipe-maintainers:
    - conda-forge/r

# Package: glmnet
# Type: Package
# Title: Lasso and Elastic-Net Regularized Generalized Linear Models
# Version: 2.0-18
# Date: 2019-05-18
# Author: Jerome Friedman [aut, cre], Trevor Hastie [aut, cre], Rob Tibshirani [aut, cre], Noah Simon [aut, ctb], Balasubramanian Narasimhan [aut, ctb], Junyang Qian [ctb]
# Maintainer: Trevor Hastie <hastie@stanford.edu>
# Depends: Matrix (>= 1.0-6), utils, foreach
# Imports: methods
# Suggests: survival, knitr, lars
# Description: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
# License: GPL-2
# VignetteBuilder: knitr
# URL: http://www.jstatsoft.org/v33/i01/.
# NeedsCompilation: yes
# Packaged: 2019-05-19 00:31:37 UTC; hastie
# Repository: CRAN
# Date/Publication: 2019-05-20 05:10:12 UTC
